library(SeqArray)
library(SNPRelate)
library(pander)
library(scales)
library(magrittr)
library(tidyverse)
library(readxl)
library(sp)
library(ggmap)
library(rgdal)
library(ggsn)
library(parallel)
library(qqman)
theme_set(theme_bw())
mc <- min(12, detectCores() - 1)
alpha <- 0.05
w <- 169/25.4
h <- 169/25.4
This workflow immediately follows 03_SNPFiltering and performs an analysis on the SNPs retained after filtering. Analysis performed is:
gdsPath <- file.path("..", "5_stacks", "gds", "populations.snps.gds")
gdsFile <- seqOpen(gdsPath, readonly = TRUE)
keepSNPs <- readRDS("keepSNPsAfterLDPruning.RDS")
sampleID <- tibble(
Sample = seqGetData(gdsFile, "sample.annotation/Sample"),
Population = seqGetData(gdsFile, "sample.annotation/Population"),
Location = seqGetData(gdsFile, "sample.annotation/Location")
)
popSizes <- sampleID %>%
group_by(Population) %>%
summarise(n = n())
genotypes <- tibble(
variant.id = seqGetData(gdsFile, "variant.id") ,
chromosome = seqGetData(gdsFile, "chromosome"),
position = seqGetData(gdsFile, "position")
) %>%
cbind(seqGetData(gdsFile, "genotype") %>%
apply(MARGIN = 3, colSums) %>%
t %>%
set_colnames(sampleID$Sample)) %>%
as_tibble() %>%
right_join(keepSNPs) %>%
gather(Sample, Genotype, -variant.id, -chromosome, -position) %>%
dplyr::filter(!is.na(Genotype)) %>%
arrange(variant.id, Sample) %>%
left_join(sampleID)
snpIn1996 <- genotypes %>%
filter(Population == 1996) %>%
group_by(variant.id) %>%
summarise(maf = mean(Genotype)) %>%
filter(maf > 0)
genotypes %<>%
right_join(snpIn1996)
seqClose(gdsFile)
lowCall <- c("gc2901", "gc2776", "gc2731", "gc2727", "gc2686")
snp4PCA <- genotypes %>%
filter(!Sample %in% lowCall) %>%
group_by(variant.id, Population) %>%
summarise(n = n()) %>%
spread(Population, n) %>%
mutate(N = `1996` + `2010` + `2012`) %>%
ungroup() %>%
filter(N > 0.95*(sum(popSizes$n) - length(lowCall)))
pca <- genotypes %>%
filter(variant.id %in% snp4PCA$variant.id) %>%
dplyr::select(variant.id, Sample, Genotype) %>%
spread(Sample, Genotype) %>%
as.data.frame() %>%
column_to_rownames("variant.id") %>%
as.matrix() %>%
apply(2, function(x){
x[is.na(x)] <- mean(x, na.rm = TRUE)
x
}) %>%
t() %>%
.[, apply(., 2, function(x){length(unique(x)) > 1})] %>%
prcomp( center = TRUE)
As noted in the previous section sample samples gc2901, gc2776, gc2731, gc2727 and gc2686 had a SNP identification rate \(< 50\)% and as such these were markd as potential outliers. Ignoring these samples, and restricting data to SNPs identified in \(>95\)% of all samples, a preliminary PCA was performed This amounted to 7,767 of the possible 18,878 SNPs for analysis using PCA. Missing values were specified as the mean MAF over all populations combined.
Given the initially observed structure, in which samples from the 2012 population are separating from the other samples which group with the 1996 population, the collection points for the 2012 samples as investigated.
sampleID %<>%
left_join(
file.path("..", "external", "GPS_Locations.xlsx") %>%
read_excel() %>%
dplyr::select(Sample, ends_with("tude")) %>%
mutate(Sample = gsub("[Oo][Rr][Aa] ([0-9ABC]*)", "ora\\1", Sample))
)
PCA showing population structure. Point size reflects the proportion of SNPs for which imputation was required, and the observed structure appeared independent of this.
loc <- c(range(sampleID$Longitude, na.rm = TRUE) %>% mean,
range(sampleID$Latitude, na.rm = TRUE) %>% mean)
saPoly <- readRDS("saPoly.RDS")
roads <- readRDS("roads.RDS")
gc <- SpatialPoints(cbind(x = 138.655972, y = -31.200305))
proj4string(gc) <- "+proj=longlat +ellps=GRS80 +no_defs"
xBreaks <- seq(138.65, 138.8, by = 0.05)
xLabs <- parse(text = paste(xBreaks, "*degree ~ E", sep = ""))
yBreaks <- seq(-31.2, -31.32, by = -0.04)
yLabs <- parse(text = paste(-yBreaks, "*degree ~ S", sep = ""))
leftN <- tibble(x = c(138.7965, 138.8, 138.8) - 0.01,
y = c(-31.2, -31.198, -31.193))
rightN <- tibble(x = c(138.8, 138.8, 138.8035) - 0.01,
y = c(-31.193, -31.198, -31.2))
ggMap <- get_map(loc, zoom = 12, maptype = "terrain", color = "bw")
zoomPlot <- ggmap(ggMap, extent = "normal", maprange = FALSE) +
geom_path(
aes(long, lat, group = group),
data = subset(roads, SURFACE == "UNSE"),
linetype = 2, size = 0.3) +
geom_path(
aes(long, lat, group = group),
data = subset(roads, SURFACE != "UNSE"),
linetype = 1, size = 0.4) +
geom_label(x = 138.74, y = -31.29, label = "Flinders Ranges NP", alpha = 0.4) +
geom_label(x = 138.72, y = -31.22, label = "Gum Creek", alpha = 0.4) +
geom_point(aes(x, y), data = as.data.frame(gc), shape = 3, size = 3) +
geom_text(aes(x, y), label = "HS", data = as.data.frame(gc), nudge_y = 0.005) +
geom_polygon(
data = subset(saPoly, F_CODE == "HD"),
aes(long, lat, group = group),
fill = rgb(1, 1, 1, 0), colour = "grey10", size = 0.3) +
geom_polygon(
data = subset(saPoly, F_CODE == "PARK"),
aes(long, lat, group = group),
fill = rgb(1, 1, 1, 0), colour = "grey10", size = 0.2) +
geom_point(
data= filter(pca4Plot, grepl("2012", Population)),
aes(Longitude, Latitude, colour = Population),
size = 0.9*ps) +
geom_polygon(aes(x, y), data = leftN, fill = "white", colour = "grey10", size = 0.4) +
geom_polygon(aes(x, y), data = rightN, fill = "grey10", colour = "grey10", size = 0.4) +
geom_text(x = 138.79, y = -31.19, label = "N",
colour = "grey10", size = 4) +
scale_colour_manual(values = popCols[2:3]) +
coord_cartesian(xlim = c(138.618, 138.81),
ylim = c(-31.335, -31.18),
expand = 0) +
scale_x_continuous(breaks = xBreaks, labels = xLabs) +
scale_y_continuous(breaks = yBreaks, labels = yLabs) +
guides(colour = FALSE) +
ggsn::scalebar(x.min = 138.618, x.max = 138.81,
y.min = -31.335, y.max = -31.18,
transform = TRUE,
dist = 2, dist_unit = "km",
model = 'GRS80',
height = 0.012, st.size = 4,
location = 'bottomright',
anchor = c(x = 138.8, y = -31.328)) +
labs(x = "Longitude",
y = "Latitude") +
theme(text = element_text(size = fs),
plot.margin = unit(c(1, 1, 1, 1), "mm"))
ausPolygon <- readRDS("ausPolygon.RDS")
ausPts <- SpatialPoints(cbind(x = loc[1], y = loc[2]))
proj4string(ausPts) <- proj4string(ausPolygon)
ausPlot <- ggplot() +
geom_polygon(data = ausPolygon,
aes(long, lat, group = group), fill = "white", colour = "black") +
geom_point(data = as.data.frame(ausPts), aes(x, y), size = 1.5) +
theme_void() +
theme(plot.background = element_rect(fill = "white", colour = "black"))
## png
## 2
Figure 1: Collection points for all 2012 samples with colours showing sub-populations initially defined by PCA analysis and k-means clustering.
zoomLoc <- c(138.753, -31.242)
xBreaks <- seq(138.74, 138.76, by = 0.01)
xLabs <- parse(text = paste(xBreaks, "*degree ~ W", sep = ""))
yBreaks <- seq(-31.235, -31.25, by = -0.005)
yLabs <- parse(text = paste(-yBreaks, "*degree ~ S", sep = ""))
central <- rbind(x = c(138.749, 138.755),
y = c(-31.2365, -31.2495)) %>%
set_colnames(c("min", "max"))
leftN <- tibble(x = c(138.7595, 138.76, 138.76),
y = c(-31.234, -31.2335, -31.2325))
rightN <- tibble(x = c(138.76, 138.76, 138.7605),
y = c(-31.2325, -31.2335, -31.234))
get_map(zoomLoc, zoom = 15, maptype = "terrain", source = "google", color = "bw") %>%
ggmap() +
geom_jitter(
data = filter(pca4Plot, grepl("2012", Population)),
aes(Longitude, Latitude, colour = Population),
size = 3, width = 0.0005, height = 0) +
geom_rect(
xmin = central["x", "min"],
xmax = central["x", "max"],
ymin = central["y", "min"],
ymax = central["y", "max"],
fill = "red", alpha = 0.01, colour = "black") +
geom_polygon(aes(x, y), data = leftN, fill = "white", colour = "grey10", size = 0.4) +
geom_polygon(aes(x, y), data = rightN, fill = "grey10", colour = "grey10", size = 0.4) +
geom_text(x = 138.76, y = -31.232, label = "N",
colour = "grey10", size = 5) +
scale_colour_manual(values = popCols[2:3]) +
scale_x_continuous(breaks = xBreaks, labels = xLabs) +
scale_y_continuous(breaks = yBreaks, labels = yLabs) +
theme_bw() +
guides(colour = FALSE) +
labs(x = "Longitude",
y = "Latitude") +
coord_cartesian(xlim = c(138.74, 138.762),
ylim = c(-31.253, -31.231),
expand = 0) +
ggsn::scalebar(
x.min = 138.74, x.max = 138.762,
y.min = -31.253, y.max = -31.231,
transform = TRUE,
dist = 0.25, dist_unit = "km",, model = 'GRS80',
height = 0.012, st.size = 4,
location = 'bottomright',
anchor = c(x = 138.761, y = -31.252)
)
Zoomed-in view of the central region for 2012 samples with colours showing sub-populations defined by PCA analysis. The region considered to be the Central Region is shaded in red. Due to overlapping GPS points a small amount of jitter has been added to the x-axis.
The structure observed within the 2012 population in the PCA could possibly be explained by recent migration into this region. As the samples collected in the outer regions appeared very similar to the 1996 population in the above plots, this would possibly indicate this a very recent event as the genetic influence of this has not spread through the wider area. Although this may be due to other factors such as sampling bias, this structure was addressed by identifying SNPs which showed an association with the sub-populations identified by PCA analysis. In this way, any candidate SNPs obtained below will be less impacted by this structure, and will be more reflective of the intended variable under study, as opposed to any internal structure of the 2012 population.
oraRegions <- pca4Plot %>%
filter(grepl("2012", Population)) %>%
rowwise() %>%
mutate(yCentral = cut(Latitude, breaks = central["y",], include.lowest = TRUE),
xCentral = cut(Longitude, breaks = central["x",], include.lowest = TRUE),
Central = (is.na(yCentral) + is.na(xCentral)) == 0) %>%
dplyr::select(Sample, Central)
This model tests:
H0: No association between genotypes and collection region
HA: An association exists between genotypes and collection region
regionResults <- genotypes %>%
filter(Population == 2012) %>%
split(f = .$variant.id) %>%
mclapply(function(x){
ft <- list(p.value = NA)
if (length(unique(x$Genotype)) > 1) {
ft <- x %>%
left_join(oraRegions) %>%
group_by(Genotype, Central) %>%
tally() %>%
spread(Genotype, n, fill = 0) %>%
column_to_rownames("Central") %>%
fisher.test()
}
x %>%
distinct(variant.id, chromosome, position) %>%
mutate(p = ft$p.value)
}, mc.cores = mc) %>%
bind_rows()
A total of 1682 SNPs were detected as showing a significant association between genotype and the collection region. Under H0, the number expected using α = 0.05 would be 943, and as this number was approximately double that expected, this was taken as evidence of this being a genuine point of concern for this dataset.
Notably, Type II errors were of principle concern in this instance, and as such every SNP with p < 0.05 in the above test was excluded from downstream analysis.
regionSNPs <- filter(regionResults, p < 0.05)$variant.id
saveRDS(regionSNPs, "regionSNPs.RDS")
Under this additional filtering step, the original set of 18878 SNPs will be reduced to 17,102 for testing by genotype and allele frequency.
In order to verify that the removal of the above SNPs removed the undesired population structure from the 2012 population, the above PCA was repeated, excluding the SNPs marked for removal. The previous structure noted in the data was no longer evident, and as such, these SNPs were marked for removal during analysis by genotype and allele frequency.
pcaPost <- genotypes %>%
filter(variant.id %in% snp4PCA$variant.id,
!variant.id %in% regionSNPs) %>%
dplyr::select(variant.id, Sample, Genotype) %>%
spread(Sample, Genotype) %>%
as.data.frame() %>%
column_to_rownames("variant.id") %>%
as.matrix() %>%
apply(2, function(x){
x[is.na(x)] <- mean(x, na.rm = TRUE)
x
}) %>%
t() %>%
.[, apply(., 2, function(x){length(unique(x)) > 1})] %>%
prcomp( center = TRUE)
pcaPost4Plot <- pcaPost$x %>%
as.data.frame() %>%
rownames_to_column("Sample") %>%
as_tibble() %>%
dplyr::select(Sample, PC1, PC2, PC3) %>%
left_join(sampleID) %>%
mutate(Cluster = kmeans(cbind(PC1, PC2, PC3), 3)$cluster) %>%
group_by(Cluster) %>%
mutate(maxY = max(Latitude, na.rm = TRUE)) %>%
ungroup() %>%
mutate(Population = case_when(
Population == 1996 ~ "1996",
Population == 2010 ~ "Outgroup (Turretfield)",
maxY == max(maxY) ~ "2012 (Outer)",
maxY != max(maxY) ~ "2012 (Central)"
)) %>%
left_join(genotypes %>%
filter(variant.id %in% snp4PCA$variant.id,
!Sample %in% lowCall) %>%
group_by(Sample) %>%
tally() %>%
mutate(imputationRate = 1 - n / nrow(snp4PCA)))
Figure 2: Principal Components Analysis showing structures before removal of SNPs denoting collection region in the 2012 population, and after removal of these SNPs
This model tests:
H0: No association between genotypes and populations
HA: An association exists between genotypes and populations
genotypeResults <- genotypes %>%
filter(Population != 2010,
!variant.id %in% regionSNPs) %>%
group_by(variant.id, Population, Genotype) %>%
tally() %>%
ungroup() %>%
split(f = .$variant.id) %>%
mclapply(function(x){
ft <- list(p.value = NA)
if (length(unique(x$Genotype)) > 1) {
ft <- x %>%
spread(Genotype, n, fill = 0) %>%
column_to_rownames("Population") %>%
dplyr::select(-variant.id) %>%
fisher.test()
}
x %>%
distinct(variant.id) %>%
mutate(p = ft$p.value)
},mc.cores = mc) %>%
bind_rows() %>%
filter(!is.na(p)) %>%
mutate(FDR = p.adjust(p, "fdr"),
adjP = p.adjust(p, "bonferroni")) %>%
arrange(p) %>%
left_join(genotypes %>%
distinct(variant.id, chromosome, position)) %>%
dplyr::select(variant.id, chromosome, position, everything())
Under the full genotype model:
| variant.id | chromosome | position | p | FDR | adjP |
|---|---|---|---|---|---|
| 103,513 | GL018705 | 1,937,359 | 6.103e-08 | 0.0009035 | 0.001047 |
| 20,241 | 3 | 90,851,512 | 1.053e-07 | 0.0009035 | 0.001807 |
| 27,266 | 4 | 89,602,973 | 1.865e-07 | 0.0009837 | 0.003201 |
| 25,051 | 4 | 35,111,855 | 2.587e-07 | 0.0009837 | 0.00444 |
| 9,251 | 2 | 2.8e+07 | 2.866e-07 | 0.0009837 | 0.004919 |
| 68,488 | 13 | 129,650,867 | 4.419e-07 | 0.001051 | 0.007583 |
| 93,489 | 19 | 30,689,426 | 4.838e-07 | 0.001051 | 0.008303 |
| 139,689 | GL019119 | 147,630 | 4.901e-07 | 0.001051 | 0.008411 |
| 142,596 | GL019332 | 5,745 | 6.122e-07 | 0.001167 | 0.01051 |
| 33,737 | 7 | 35,584,375 | 9.805e-07 | 0.001583 | 0.01683 |
| 13,016 | 2 | 125,603,458 | 1.014e-06 | 0.001583 | 0.01741 |
| 102,846 | GL018704 | 1,294,009 | 1.387e-06 | 0.001984 | 0.02381 |
| 115,793 | GL018751 | 737,318 | 1.837e-06 | 0.002395 | 0.03153 |
| 64,613 | 13 | 45,086,624 | 2.028e-06 | 0.002395 | 0.0348 |
| 59,189 | 12 | 60,113,885 | 2.094e-06 | 0.002395 | 0.03593 |
Figure 3a: Manhattan plot showing results for all SNPs on chromosomes 1:21 for analysis by genotype. The horizontal line indicates the cutoff for an FDR of 5%, with SNPs with an adjusted p-value < 0,05 shown in green
This model tests:
H0: No association between allele frequencies and populations
HA: An association exists between allele frequencies and populations
alleleResults <- genotypes %>%
filter(Population != 2010,
!variant.id %in% regionSNPs) %>%
group_by(variant.id, Population) %>%
summarise(P = sum(2 - Genotype),
Q = sum(Genotype)) %>%
ungroup() %>%
split(f = .$variant.id) %>%
mclapply(function(x){
m <- as.matrix(x[c("P", "Q")])
ft <- list(p.value = NA)
if (length(m) == 4) {
ft <- fisher.test(m)
}
x %>%
mutate(MAF = Q / (P + Q)) %>%
dplyr::select(variant.id, Population, MAF) %>%
spread(Population, MAF) %>%
mutate(p = ft$p.value)
},mc.cores = mc) %>%
bind_rows() %>%
filter(!is.na(p)) %>%
rename(MAF_1996 = `1996`,
MAF_2012 = `2012`) %>%
mutate(FDR = p.adjust(p, "fdr"),
adjP = p.adjust(p, "bonferroni")) %>%
arrange(p) %>%
left_join(genotypes %>%
distinct(variant.id, chromosome, position)) %>%
dplyr::select(variant.id, chromosome, position, everything())
Under this model:
| variant.id | chromosome | position | MAF_1996 | MAF_2012 | p | FDR | adjP |
|---|---|---|---|---|---|---|---|
| 103,513 | GL018705 | 1,937,359 | 0.2255 | 0.009615 | 3.44e-07 | 0.005911 | 0.005916 |
| 27,266 | 4 | 89,602,973 | 0.1961 | 0 | 6.875e-07 | 0.005911 | 0.01182 |
| 93,489 | 19 | 30,689,426 | 0.1863 | 0 | 1.531e-06 | 0.007628 | 0.02632 |
| 9,251 | 2 | 2.8e+07 | 0.25 | 0.0122 | 1.866e-06 | 0.007628 | 0.03209 |
| 31,004 | 6 | 9,037,073 | 0.4432 | 0.1277 | 2.52e-06 | 0.007628 | 0.04333 |
| 25,051 | 4 | 35,111,855 | 0.2907 | 0.04082 | 3.086e-06 | 0.007628 | 0.05307 |
| 13,016 | 2 | 125,603,458 | 0.1915 | 0 | 3.105e-06 | 0.007628 | 0.0534 |
| 68,488 | 13 | 129,650,867 | 0.2708 | 0.03333 | 4.172e-06 | 0.008366 | 0.07174 |
| 59,189 | 12 | 60,113,885 | 0.1633 | 0 | 4.749e-06 | 0.008366 | 0.08166 |
| 102,846 | GL018704 | 1,294,009 | 0.2065 | 0.0102 | 4.865e-06 | 0.008366 | 0.08366 |
| 139,689 | GL019119 | 147,630 | 0.2736 | 0.04167 | 5.576e-06 | 0.008717 | 0.09589 |
| 133,433 | GL018933 | 204,058 | 0.01754 | 0.2019 | 7.749e-06 | 0.0111 | 0.1333 |
| 33,737 | 7 | 35,584,375 | 0.2841 | 0.04348 | 9.725e-06 | 0.01286 | 0.1672 |
| 115,793 | GL018751 | 737,318 | 0.2609 | 0.03261 | 1.275e-05 | 0.01447 | 0.2193 |
| 142,596 | GL019332 | 5,745 | 0.3077 | 0.05814 | 1.314e-05 | 0.01447 | 0.226 |
| 64,613 | 13 | 45,086,624 | 0.2609 | 0.03409 | 1.412e-05 | 0.01447 | 0.2429 |
| 2,227 | 1 | 64,635,286 | 0.186 | 0 | 1.43e-05 | 0.01447 | 0.2459 |
| 131,672 | GL018907 | 283,766 | 0.1932 | 0.01 | 1.627e-05 | 0.01495 | 0.2798 |
| 81,518 | 16 | 56,527,308 | 0.05814 | 0.3043 | 1.652e-05 | 0.01495 | 0.284 |
| 138,810 | GL019077 | 34,265 | 0.2232 | 0.02941 | 2.279e-05 | 0.01959 | 0.3919 |
| 60,986 | 12 | 141,844,290 | 0.08889 | 0.3556 | 2.435e-05 | 0.01987 | 0.4187 |
| 88,912 | 18 | 25,311,176 | 0.1731 | 0.4434 | 2.542e-05 | 0.01987 | 0.4371 |
| 74,214 | 14 | 97,813,263 | 0.02083 | 0.2021 | 4.48e-05 | 0.0335 | 0.7704 |
| 91,957 | 19 | 13,055,154 | 0.03191 | 0.234 | 5.416e-05 | 0.03776 | 0.9314 |
| 138,713 | GL019084 | 74,038 | 0.1961 | 0.02041 | 5.49e-05 | 0.03776 | 0.9441 |
| 136,355 | GL018985 | 129,495 | 0.1702 | 0.01042 | 6.686e-05 | 0.04259 | 1 |
| 136,354 | GL018985 | 123,328 | 0.3636 | 0.1275 | 6.797e-05 | 0.04259 | 1 |
| 58,800 | 12 | 40,509,779 | 0.3214 | 0.5943 | 7.288e-05 | 0.04259 | 1 |
| 32,502 | 7 | 2,702,919 | 0.2143 | 0.03774 | 7.807e-05 | 0.04259 | 1 |
| 92,678 | 19 | 19,178,148 | 0.22 | 0.03261 | 7.861e-05 | 0.04259 | 1 |
| 62,212 | 13 | 2,979,383 | 0.01818 | 0.1731 | 8.051e-05 | 0.04259 | 1 |
| 122,683 | GL018791 | 957,960 | 0.163 | 0.009615 | 8.094e-05 | 0.04259 | 1 |
| 78,185 | 15 | 87,473,844 | 0.3511 | 0.1154 | 8.416e-05 | 0.04259 | 1 |
| 77,077 | 15 | 43,156,117 | 0.1531 | 0.4057 | 8.594e-05 | 0.04259 | 1 |
| 131,070 | GL018883 | 283,912 | 0.3654 | 0.125 | 8.683e-05 | 0.04259 | 1 |
| 140,313 | GL019154 | 883 | 0.234 | 0.04082 | 8.916e-05 | 0.04259 | 1 |
| 20,241 | 3 | 90,851,512 | 0.4375 | 0.1667 | 9.731e-05 | 0.04522 | 1 |
| 93,211 | 19 | 26,020,372 | 0.1939 | 0.02083 | 0.0001019 | 0.04612 | 1 |
## png
## 2
Manhattan plot showing results for all SNPs on chromosomes 1:21 when analysing by allele frequencies. The horizontal line indicates the cutoff for an FDR of 5%, with SNPs considered significant under the Bonferroni adjustment shown in green.